Alternate Design/Alternate Bid Process for Pavement-Type Selection (2017)

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16 CHAPTER THREE ALTERNATE DESIGN/ALTERNATE BID LIFE-CYCLE COST ANALYSIS INTRODUCTION Tim Waight, the deputy director of the Texas Turnpike Author- ity, stated, “Selecting the pavement type is the number one issue that drives the cost of a major highway project, and that decision then drives all the other engineering decisions that impact a project’s actual life cycle cost” (T. Waight, personal communication, July 1, 2003). ADAB permits head-to-head competition between asphalt and concrete (Cross and Parsons 2001) by allowing the contrac- tors and/or design-builders to bid and/or propose the pavement that provides the most economic option in the marketplace at the timing of letting. To permit a fair and equitable award decision, the dynamics of the competing life cycles must first be reconciled. Research has found that including LCCs in the pavement-type selection decision can be influenced by the selec- tion of key input parameters such as the analysis period and the discount rate (Gransberg 2004). To do this effectively without injecting unintentional bias, the sensitivity of each life-cycle input variable must be understood to ensure that its impact on the LCCA output is not unduly influenced by the owner’s selection of parameters for the LCC adjustment factor. The FHWA article (2015) “Improving Transportation Investment Decisions” posits that an LCCA provides a structured methodology that can be used to calculate the alternative cost differential between construction alternatives by analyzing ini- tial costs and discounted future costs that include at least one rehabilitation. Another stated benefit is that the analysis provides the information and documentation needed for successful public dialogue. It is advantageous for public owners to implement LCC-based ADAB for the delivery of highway pavement projects, for several reasons. First, DOT agencies are generally prohibited by law from awarding construction projects until sufficient funding is on hand (FHWA 1993). A primary issue for infrastructure is funding the large number of much-needed mainte- nance and repair projects (ASCE 2013). Failure to incorporate the LCC in a project’s design moves the need for funding to the operations and maintenance (O&M) period. Complex public transportation projects also require the simultaneous evalu- ation of many interrelated variables (Ashley et al. 1998), which can complicate the adoption of new award processes unless a degree of objectivity in their contract award methodology can be demonstrated. By selecting ADAB, the paradigm for pave- ment design can shift from minimize first cost to minimize life-cycle cost. ADAB gives the public owner a clear methodology and analytical mechanism for assigning a value to the benefit of paying for a longer-lasting design and thus can measure the enhanced design alternative’s differential value compared with other alternatives. Use of a predetermined mathematical sys- tem for identifying best value increases the level of objectivity during procurement and makes the selection/award process transparent to the competitors by clearly providing the definition of success for a given pavement project. Presumably, shifting to LCC-based awards may also facilitate the reduction in long-term O&M costs, allowing DOTs to more effectively utilize their maintenance budget and enhance the long-term quality of their highways. LIFE-CYCLE COST ADJUSTMENT FACTORS The LCC adjustment factor is the difference between the NPV of future O&M activities for two pavement alternatives. ADAB project reports on the SEP-14 website emphasize the importance of using a thoughtful process when developing an LCC-based bid adjustment factor to not only ensure that the factor treats both HMA and PCC alternatives fairly, but also to ensure that potential bidders perceive the contract bidding and award process as equitable. Of 16 respondents that use ADAB, six stated that they do not use an adjustment factor during the ADAB bidding process. One potential advantage of not using an adjust- ment factor is the elimination of time-variable factors that are often used to develop the adjustment factor. “If all engineering factors could be properly modeled and all costs properly compared and discounted to present value, the ultimate lowest cost pavement of whatever type or design would be the proper pavement to construct.” AASHTO Guide for Design of Pavement Structures (1993)

17 The literature, case studies, and survey showed that there are at least seven different approaches to developing an LCC- based bid adjustment factor. Table 4 consolidates those findings and shows which DOTs are known to use them. Table 4 shows the number of options available to DOTs for the development of a rational adjustment factor and leads to the conclusion that there is no single correct method for accounting for the difference in pavement-type service lives, post- construction O&M costs, or the amount of disruption the public will experience during construction. One interesting survey finding is that several states do not adjust the bid pricing and use direct competition between the pavement types by eliminat- ing the use of an adjustment factor. TABLE 4 LCC-BASED BID ADJUSTMENT FACTOR SUMMARY Name Formula Highway Agencies A+C Bidding; C applied to HMA only HMA Bid = HMA Contract Bid Amount + NPW of Future HMA Rehab PCC Bid = PCCP Contract Bid Amount KS, MT, OK A+C Bidding; C applied to both HMA Bid = HMA Contract Bid Amount + NPW of Future HMA Rehab PCC Bid = PCC Contract Bid Amount + NPW of Future PCC Rehab IN A+B+C Bidding HMA Bid = HMA Contract Bid Amount + Value of Time + NPW of Future HMA Rehab PCC Bid = PCC Contract Bid Amount + Value of Time + NPW of Future PCC Rehab KY, LA A+C + Lane Rental HMA EUAC = [HMA Contract Bid Amount + Lane Rental + Future HMA Maint. Costs] (capital recovery factor at OMB discount rate for 26 years) PCC EUAC = PCC Contract Bid Amount + Lane Rental + Future PCC Maint. Costs] (capital recovery factor at OMB discount rate for 26 years) MI Adjustment Factor Adjustment Factor = NPW Future Asphalt Rehab - PW Future Concrete Rehab Low bidder = lower of (PCC Bid Price) vs. (HMA Bid Price + Adjustment Factor) (Assuming asphalt has higher NPW M&R costs) MO LCC Advantage Low bidder = lower of (PCC Bid Price + NPW Future Concrete M&R) vs. (HMA Bid Price + NPW Future HMA M&R) ON A – D (Alternative Differential) Bidding Adjustment Factor = Fixed Value set by DOT for each project Low bidder = lower of (PCC Bid Price - Adjustment Factor) vs. HMA Bid Price (Assuming asphalt has higher NPW M&R costs) IA No adjustment Low bidder = lower of PCC Bid Price vs. HMA Bid Price AL, AR, FL, NC, OH, WV LIFE-CYCLE COST ANALYSIS ISSUES IN ALTERNATE DESIGN/ALTERNATE BID Government agencies and the pavement industry have developed various forms of LCCA software and each uses different assumptions in the LCCA models to compare pavement alternatives. A 2004 review of LCCA software highlights the need for objective determination and selection of LCCA input parameters based on sensitivity analyses (Gransberg 2004). Fundamental LCCA principles are rooted in classic engineering economic theory. LCCA is very easy to understand on a fundamental basis, yet it can be very difficult to confidently apply to long-term infrastructure projects (Roark 2011). Initially, the application of LCCA was developed for evaluating machinery purchase options where the service life is much shorter than a highway’s and where the machine has better-defined salvage value as well as a statutory method for calculating deprecia- tion (Reynolds et al. 2015). Expanding the LCCA models and theory to pavement assets that will last for decades brings into question whether discounting present values accurately estimates their amounts over such long periods (Pittenger et al. 2012). Thus, it becomes important to have logical, justifiable, and defensible inputs to the ADAB LCCA procedure. This assertion is validated because six of the DOTs that responded to the survey indicated that they do not use an LCC-based adjustment factor on their ADAB projects. These six states have used ADAB for periods ranging from 1.5 to 25 years. Combined, these states have an average of approximately 9.3 years of experience with ADAB procedures. As a result of the previous discussion, this section will briefly cover the following issues affecting the development of an LCC adjustment factor for ADAB projects:

18 1. Selection of a method of analysis, 2. Selection of an analysis period, 3. Selection of an appropriate discount rate, 4. Calculation of residual/salvage value, 5. Calculation of user costs, 6. Use of the same discount rate for both agency and user costs, and 7. Failure to consider the value of cost certainty when comparing materials with different levels of historic volatility. LCCA Methods Many states and federal government agencies recommend using an LCCA as part of the pavement-type selection process (Walls and Smith 1998). Often, the LCCA requires a calculation based on net present worth (NPW) and each alternative is analyzed using the same analysis period. The analysis period is recommended to contain at least one major rehabilitation. Alternately, a single period can be selected, but then a residual value for an alternative with remaining service life must be calculated at the end of the period. Challenges related to performing an LCCA were found in the literature and include the following: 1. Replication of service lives is an artificial computational device that fails to model actual circumstances (Hall et al. 2007). 2. Calculation of residual value for most assets is difficult without empirical deterioration models (Temple et al. 2004; Roark 2011). The literature typically expresses pavement service lives as recommended ranges and are often fixed by professional judgment or agency policy and not from a rigorous analysis of deterioration (Hall et al. 2007). The Ontario MTO began using LCCAs in its pavement-type selection process in the 1980s. MTO compares pavement design alternatives based on NPW analysis over a 50-year period. Concrete pavements are assumed to have an initial service life of 28 years until the first rehabilitation; asphalt pavements are assumed to have an initial service life of 19 years (Tighe 2001). The Michigan DOT (MDOT) has chosen the equivalent uniform annual cost (EUAC) approach for its ADAB program (Youngs and Krom 2009). Whether NPW or EUAC is used, ensuring a rational value for the period over which asset alternatives are analyzed becomes an important issue that will influence final LCCA results. Analysis Period and Sensitivity Previous research has found that LCCA outcomes are very sensitive to the period over which they are analyzed and that the selection of the period can in effect bias the LCCA output (Gransberg and Molenaar 2004; Ellis 2012; Mack et al. 2012; Pittenger et al. 2012). The analysis period is the performance period over which alternatives are evaluated, which should be long enough to cover at least one major rehabilitation cycle (Walls and Smith 1998; FHWA 2012). The following are the four options for selecting an analysis period: 1. “The least common multiple whose issues were covered in the previous section, 2. The shortest service life of all alternatives, 3. The longest service life of all alternatives, 4. A fixed period based on a mandate, regulation, or policy” (Gransberg and Scheepbouwer 2010). In some cases, agencies add user costs to the LCCA model for a project that is located in a congested urban area. This can help account for differences in construction durations between alternatives and, as such, accrues the otherwise unrecognized benefit of accelerated construction schedules with the agency reconstruction cost.

19 The analyst must be aware of the inability to “compare apples to apples” owing to fundamental differences between pave- ment types and the many combinations of design inputs, economic factors, and analysis assumptions. Sensitivity analyses are recommended to illustrate how various factors influence the alternative selection. Discount Rate Selection Issues The discount rate accounts for the time value of money in the analysis. It is used to compare alternative uses of funds by reducing the future expected costs to present-day terms (Hallin et al. 2011). Undergraduate engineering economics textbooks commonly present examples of comparing two alternatives over a given analysis period using the same given discount rate (Newnan and Lavelle 2013), but the textbook examples are simplistic compared with the challenges encountered in pavement engineering. In practice, the selection of each parameter influences the analysis and requires justification. For example, the discount rate reduces the value of future costs based on the time-value of money. One study showed that higher discount rates tend to favor a flexible pavement alternative, whereas lower discount rates may favor a rigid pavement design (Wimsatt et al. 2009). Based on this finding, a sensitivity analysis including the discount rate is needed to determine the impact on the LCCA model. Many agencies use a rate similar to FHWA recommendations, but no consensus was found. For example, Kentucky uses a variable discount rate that is tied to a national index and Pennsylvania uses a long-term state bond rate (Corotis and Gransberg 2005). The New Zealand discount rate was recently lowered from 10% to 8% and the analysis period was increased from 25 to 30 years (Gransberg and Scheepbouwer 2010). Canadian provincial MTOs use rates that are in line with those used in the United States. However, the national equivalent of FHWA in Canada, Australia, and New Zealand use rates that are higher than those currently used by U.S. agencies. Figure 3 comes from a research project that looked at how sensitive pavement LCCA design decisions were to the selection of a discount rate for a Washington DOT project (Corotis and Gransberg 2005). Each pavement type produced a different first cost and different LCCs of maintenance and rehabilitation. The FHWA LCCA method detailed in the Interim Technical Bulletin was used to calculate the LCCs for each alternative (Walls and Smith 1998). The lower LCC changes at a discount rate of roughly 5% where the HMA alternate is preferred over the PCC alternative. This work coincides with the findings from Wimsatt et al. (2009). This very same phenomenon was observed by Tighe (2001) in Canada and by Waters and Pidwerbesky (2006) in New Zealand. FIGURE 3 Discount rate sensitivity example (Corotis and Gransberg 2005).

20 Residual/Salvage Value and Remaining Service Life The two components to estimating the value of an asset at the end of an analysis period are as follows (Hallin et al. 2011): • Residual value, which is the value of the in-place pavement materials at the end of their service lives less the cost to remove and process the materials for reuse; and • Remaining service life, which is the structural life remaining in the pavement at the end of the analysis period. The literature details the challenges associated with the proper and representative development of a residual or salvage value for infrastructure assets (Walls and Smith 1998; Tighe 2001; Gransberg and Scheepbouwer 2010; Pittenger et al. 2012). The applicability of current systems for calculating residual value based on depreciation theories is a primary concern (Gransberg and Scheepbouwer 2010). The FHWA Asset Management Primer (1999) recommends the use of straight-line depreciation, whereas other nations, such as Canada and New Zealand, use empirically derived deterioration models to determine the residual/salvage value. MoDOT, which has an ongoing and active ADAB program, takes the position that because there is “no practical means to predict what the salvage value would be at the end of the design period for either pavement …” it does not include a residual/ salvage value in LCCA (Rozycki and McCullough 2008; Ahlvers 2010; Roark 2011). User Cost Calculation “User delay cost is another potentially controversial item that continues to be evaluated” (Temple et al. 2004). An early report on the topic of user costs (Hicks and Epps 2000) found user costs can exceed $10,000 per lane mile/day. This value would calculate user costs on a conventional eight-lane urban freeway to be $80,000 per mile per day, and an LCCA of a hypotheti- cal 5-mi upgrade would project user costs of $400,000/day. When calculated in this manner, it is possible for the user costs themselves to be more than the agency’s cost for the construction and maintenance. The discrepancy between costs has led some authors to recommend that a “weighted” amount of user costs be factored into the LCCA (Hall et al. 2007). In this case, for a project in a heavily congested urban area with enormous potential user costs, the analyst might choose to give a 10% weight the user costs, making the agency costs a 90% weight. Careful justification and consideration may be given to the weighted factors as the LCCA outcome can change depending on the weighting. High user costs induce a bias toward design alternatives that can be built faster and have longer service lives (Hall et al. 2007). In the survey responses, 60% of the agencies included user costs in their LCCA methodology. Within the agencies that con- sider user costs, roughly half add the user costs directly to the agency costs and the other half consider user costs separately. Again the survey shows no consensus as to whether user costs should be included in the ADAB process. Challenges associated with user costs can be summarized as follows: It may also be undesirable to the agency to attempt to consider all user costs components in the analysis, or to weight them equally with agency costs. Particularly for high-volume facilities, estimated user costs may be so high as to mask any other significant cost differences among alternatives. This is undesirable because the agency costs are those for which the agency really must program its funds. [Hall et al. 2007 (italics added)] The conflict appears from the literature to be a philosophical balancing act between the analyst’s desire to be as complete as possible in the LCCA model and the analyst’s ability to generate accurate input data, without resorting to simplifying assump- tions that may unintentionally skew the outcome. Using the Same Discount Rate for Both Agency and User Costs The World Bank differentiates between real monetary costs and benefits and intangible costs and benefits such as user costs by the application of a “social discount rate” to evaluate the impact of infrastructure projects (Lopez 2008). Corotis and Gransberg (2005) completed an analysis on a DB project in Minnesota where ADAB was used. The study discounted the agency costs using a constant financial rate of 4% and discounted the user costs over the World Bank’s recommended social discount rate range between 0% and 10%. The analysis compared HMA and PCC equivalent pavement alternatives. It found that the PCC option had the lowest LCC when the social discount rate was less than 4.5%. If it was more than 4.5%, the HMA option became the preferred alternative.

21 Value of Cost Certainty The final LCCA issue deals with certainty of the models’ input values over the time period from which the pavement-type decision is made until the bids are opened and construction can commence. For a project that is in design and will not be con- structed for several years, it is common practice to include a contingency in a cost estimate to account for the uncertainty in construction material prices. Conversely, the accepted practice for a pavement LCCA on projects covering a period of decades is done “without a thought of the value of differential cost certainty inherent to the different design alternatives assessed in the LCCA” (Gransberg and Scheepbouwer 2010). In 2008, the Utah DOT report indicated that, in that year, construction pricing for HMA rose 25.9% while PCC prices rose only 3.6%. This example illustrates the potential cost savings of ADAB because it provides a means to capture those market-driven price differences by including them in the procurement process. The survey found that 80% of DOTs that use alternate bidding also use an LCCA-based pavement-type selection methodology for all paving projects. Of that sample, roughly half use a deterministic LCCA and the rest rely on stochastic methodology to determine LCC. The FHWA guidance (Walls and Smith 1998) suggests that both are appropriate. SUMMARY Conclusions The conclusions that can be drawn with regard to an LCCA used as a part of ADAB procurement and described as follows: 1. Many factors can influence LCCA, and the literature routinely emphasizes the importance of justifying the selected parameters and incorporating them into a sensitivity analysis. 2. Some survey respondents have implemented ADAB without the use of an adjustment factor. Effective Practices Two effective practices were identified in the previous analysis. First, six DOTs that use ADAB do not include an LCC-based adjustment factor. This approach simplifies the process and removes many, if not all, of the technical issues, such as discount rate, for which assumptions must be made. Second, for agencies that use an LCC-based bid adjustment factor, the use of a robust sensitivity analysis of all input assumptions is indicated to ensure that the result is not unintentionally biased by the input values. Future Research No future research was identified by the analyses found in this chapter.